An AI development company that treats production as the product.
Developers Core helps startups design and ship applied AI — retrieval systems, agents, vision pipelines, and LLM features — with the engineering discipline required after the first happy-path demo. UK-registered. Fixed-scope engagements.
Capability detail
Retrieval-augmented generation
Corpus design, chunking strategy, metadata filters, hybrid search, citation-first answers, refusal behaviour, and evaluation harnesses so quality does not silently regress.
- — Document ingestion pipelines
- — Jurisdiction / tenant isolation
- — Groundedness evals
- — Cost and latency budgets
Agents and tool-calling systems
Agents that call internal APIs with least privilege, durable state, human approval gates, and observability — not unbounded loops that email your customers by accident.
- — Tool schemas & auth boundaries
- — Workflow orchestration
- — Audit logs
- — Failure recovery
Computer vision & imaging
Detection, segmentation, and domain imaging pipelines — including healthcare-adjacent work where explainability and data handling are part of the product.
- — Model selection & fine-tuning
- — DICOM / domain I/O
- — Saliency / explainability
- — GPU inference paths
LLM features inside products
Copilots, extraction, classification, and automation wired into React/Node/Python codebases with product UX, rate limits, and monitoring.
- — Prompt + policy design
- — Streaming UX
- — Guardrails
- — A/B and feedback loops
Platform & cloud engineering
When AI work requires real infrastructure: APIs, queues, multi-region, IaC, and the unsexy reliability work that keeps customers.
- — FastAPI / Node services
- — AWS / Azure
- — Terraform
- — Observability
Product engineering
Full-stack delivery when the AI feature is useless without the surrounding product — auth, billing, dashboards, mobile, and admin tools.
- — Next.js / React Native
- — SaaS multi-tenant
- — Payments
- — Design systems
Proof in production contexts
FinTech · Conversational AI
AI voice agent for FinTech support
Automating high-volume tier-1 support with low-latency conversational AI and secure ledger integration.
Healthcare · Computer Vision
HIPAA-ready AI radiology triage
Explainable medical imaging AI that flags anomalies faster while satisfying clinical governance and data safeguards.
PropTech · RAG
Jurisdiction-aware permitting RAG
A retrieval assistant that guides US users through construction permitting rules by location — grounded answers, not generic chat.
SaaS · Applied LLM
LogicLinksAI — AI search visibility SaaS
A live SaaS helping businesses understand and improve how they appear in answers from ChatGPT, Claude, and Perplexity.
Media · Applied AI
ViralClip — live Twitch viral moment clipping
An AI pipeline that watches live streams, scores viral moments, and generates shareable clips for streamers and clip channels.
When AI projects should not start
There is no source of truth for the model to retrieve from — and no plan to create one.
Success is defined as ‘looks cool in a slide’ rather than a measurable user or ops outcome.
You need a research lab, not a product team (we will say so).
Budget only supports marketplace prototyping; production ownership is unfunded.
Stack we reach for first
AI / ML
- PyTorch
- LangChain / LlamaIndex
- OpenAI
- Anthropic
- Vector databases
- MONAI
Backend
- FastAPI
- Node.js
- PostgreSQL
- Redis
- Kafka
- ClickHouse
Cloud
- AWS
- Azure
- Docker
- Kubernetes
- Terraform
Scope an AI sprint
Feature Sprint engagements typically land between $4k–$8k over 3–4 weeks for a single production capability.